Identification of new M31 star cluster candidates from PAndAS images using convolutional neural networks
Shoucheng Wang, Bingqiu Chen, Jun Ma, Qian Long, Haibo Yuan, Dezi Liu,, Zhimin Zhou, Wei Liu, Jiamin Chen, and Zizhao He

TL;DR
This study employs convolutional neural networks to efficiently identify new star cluster candidates in M31 from the PAndAS survey, resulting in a catalog of 117 high-confidence candidates, including young disk clusters and halo globular clusters.
Contribution
The paper introduces a CNN-based method for large-scale star cluster candidate identification in M31, achieving high accuracy through human verification and discovering new clusters in different regions.
Findings
117 new M31 star cluster candidates identified.
Most new candidates are young clusters in the M31 disk.
Eight halo globular cluster candidates discovered.
Abstract
Context.Identification of new star cluster candidates in M31 is fundamental for the study of the M31 stellar cluster system. The machine-learning method convolutional neural network (CNN) is an efficient algorithm for searching for new M31 star cluster candidates from tens of millions of images from wide-field photometric surveys. Aims.We search for new M31 cluster candidates from the high-quality - and -band images of 21,245,632 sources obtained from the Pan-Andromeda Archaeological Survey (PAndAS) through a CNN. Methods.We collected confirmed M31 clusters and noncluster objects from the literature as our training sample. Accurate double-channel CNNs were constructed and trained using the training samples. We applied the CNN classification models to the PAndAS - and -band images of over 21 million sources to search new M31 cluster candidates. The CNN predictions were…
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